Submission¶
Put the ipynb file and html file in the github branch you created in the last assignment and submit the link to the commit in brightspace
In [252]:
from plotly.offline import init_notebook_mode
import plotly.io as pio
import plotly.express as px
init_notebook_mode(connected=True)
pio.renderers.default = "plotly_mimetype+notebook"
In [253]:
#load data
df = px.data.gapminder()
df.head()
Out[253]:
| country | continent | year | lifeExp | pop | gdpPercap | iso_alpha | iso_num | |
|---|---|---|---|---|---|---|---|---|
| 0 | Afghanistan | Asia | 1952 | 28.801 | 8425333 | 779.445314 | AFG | 4 |
| 1 | Afghanistan | Asia | 1957 | 30.332 | 9240934 | 820.853030 | AFG | 4 |
| 2 | Afghanistan | Asia | 1962 | 31.997 | 10267083 | 853.100710 | AFG | 4 |
| 3 | Afghanistan | Asia | 1967 | 34.020 | 11537966 | 836.197138 | AFG | 4 |
| 4 | Afghanistan | Asia | 1972 | 36.088 | 13079460 | 739.981106 | AFG | 4 |
Question 1:¶
Recreate the barplot below that shows the population of different continents for the year 2007.
Hints:
- Extract the 2007 year data from the dataframe. You have to process the data accordingly
- use plotly bar
- Add different colors for different continents
- Sort the order of the continent for the visualisation. Use axis layout setting
- Add text to each bar that represents the population
In [254]:
# YOUR CODE HERE
pop_conti = df[df['year'] == 2007].groupby(['continent'])['pop'].sum()
fig = px.bar(pop_conti, x = pop_conti.values, y = pop_conti.index, color = pop_conti.index)
fig.update_layout(xaxis_title = 'pop')
fig.show()
In [255]:
# YOUR CODE HERE
# update
fig.update_yaxes(categoryorder = "max ascending")
fig.show()
Question 3:¶
Add text to each bar that represents the population
In [256]:
# # YOUR CODE HERE
# See https://plotly.com/python/text-and-annotations/#multiple-annotations, chapter "Text Case"
pop_abb = list(map(lambda x: f"{x/1e9:.1f}B" if x >= 1e9 else f"{x/1e6:.0f}M", list(pop_conti.values)))
fig = px.bar(pop_conti, x = pop_conti.values, y = pop_conti.index, color = pop_conti.index,
text = pop_abb)
fig.update_yaxes(categoryorder = "max ascending")
Question 4:¶
Thus far we looked at data from one year (2007). Lets create an animation to see the population growth of the continents through the years
In [243]:
# YOUR CODE HERE
pop_conti = df.groupby(['continent','year'])['pop'].sum()
pop = list(pop_conti.values)
conti = list(pop_conti.index.get_level_values(0)) # Indexing the first index in a MultiIndex object by .get_level_values(0)
year = list(pop_conti.index.get_level_values(1))
fig = px.bar(pop_conti, x = pop, y = conti, color = conti,
text = list(map(lambda x: f"{x/1e9:.1f}B" if x >= 1e9 else f"{x/1e6:.0f}M", pop)),
animation_frame = year,
range_x = [0, 4e9])
# text_auto = True show 3.8G, which is d3 default. I dont wish to override d3 format.
fig.update_layout(xaxis_title = 'pop', yaxis_title = 'continent')
fig.update_yaxes(categoryorder = "max ascending")
fig.show()
Question 5:¶
Instead of the continents, lets look at individual countries. Create an animation that shows the population growth of the countries through the years
In [244]:
# YOUR CODE HERE
pop_conti = df.groupby(['country','year'])['pop'].sum()
pop = list(pop_conti.values)
conti = list(pop_conti.index.get_level_values(0)) # Indexing the first index in a MultiIndex object by .get_level_values(0)
year = list(pop_conti.index.get_level_values(1))
fig = px.bar(pop_conti, x = pop, y = conti, color = conti,
animation_frame = year,
range_x = [0, 1.5e9])
fig.update_layout(xaxis_title = 'pop', yaxis_title = 'country',
showlegend=False)
fig.update_yaxes(categoryorder = "max ascending")
fig.show()
Question 6:¶
Clean up the country animation. Set the height size of the figure to 1000 to have a better view of the animation
In [245]:
# YOUR CODE HERE
fig.update_layout(height = 1000)
In [246]:
# YOUR CODE HERE
fig.update_yaxes(range=(131.5, 141.5), automargin = True) # try...
In [247]:
fig.update_yaxes(minallowed = 132)